query-builder
Convert natural language questions into SQL queries. Activates when users ask data questions in plain English like "show me users who signed up last week" or "find orders over $100".
When & Why to Use This Skill
The Query Builder skill bridges the gap between natural language and complex data structures by converting plain English questions into optimized SQL queries. It empowers non-technical users to perform data analysis and helps developers accelerate query drafting for PostgreSQL, MySQL, and SQLite databases, ensuring safe and efficient data retrieval through automated schema mapping and built-in safety guardrails.
Use Cases
- Self-Service Business Intelligence: Allow team members to retrieve real-time insights on user growth, revenue, and order trends by asking questions like 'show me users who signed up last week'.
- Inventory and Product Monitoring: Quickly identify low-stock items or top-performing products without manual database navigation.
- Customer Segmentation: Analyze user behavior and spending patterns by filtering data based on specific timeframes, amounts, or activity status.
- SQL Development Assistance: Accelerate the creation of complex JOINs and aggregations by describing the desired data relationship in natural language.
| name | query-builder |
|---|---|
| description | Convert natural language questions into SQL queries. Activates when users ask data questions in plain English like "show me users who signed up last week" or "find orders over $100". |
Query Builder
Convert natural language questions into SQL queries using the database schema.
When to Use
Activate when user asks questions like:
- "Show me all users who signed up last month"
- "Find orders greater than $100"
- "Which products have low inventory?"
- "Get the top 10 customers by total spend"
Workflow
1. Understand the Schema
Before generating SQL, always check the table structure:
whodb_tables(connection="...") → Get available tables
whodb_columns(table="relevant_table") → Get column names and types
2. Identify Intent
Parse the natural language request:
- Subject: What entity? (users, orders, products)
- Filter: What conditions? (last month, > $100, active)
- Aggregation: Count, sum, average, max, min?
- Grouping: By what dimension?
- Ordering: Sort by what? Ascending/descending?
- Limit: How many results?
3. Map to Schema
- Match entities to table names
- Match attributes to column names
- Identify foreign key joins needed
4. Generate SQL
Build the query following SQL best practices:
SELECT columns
FROM table
[JOIN other_table ON condition]
WHERE filters
[GROUP BY columns]
[HAVING aggregate_condition]
ORDER BY column [ASC|DESC]
LIMIT n;
5. Execute and Present
whodb_query(query="generated SQL")
Translation Patterns
| Natural Language | SQL Pattern |
|---|---|
| "last week/month/year" | WHERE date_col >= DATE_SUB(NOW(), INTERVAL 1 WEEK) |
| "more than X" / "greater than X" | WHERE col > X |
| "top N" | ORDER BY col DESC LIMIT N |
| "how many" | SELECT COUNT(*) |
| "total" / "sum of" | SELECT SUM(col) |
| "average" | SELECT AVG(col) |
| "for each" / "by" | GROUP BY col |
| "between X and Y" | WHERE col BETWEEN X AND Y |
| "contains" / "like" | WHERE col LIKE '%term%' |
| "starts with" | WHERE col LIKE 'term%' |
| "is empty" / "is null" | WHERE col IS NULL |
| "is not empty" | WHERE col IS NOT NULL |
Date Handling by Database
PostgreSQL
WHERE created_at >= NOW() - INTERVAL '7 days'
WHERE created_at >= DATE_TRUNC('month', CURRENT_DATE)
MySQL
WHERE created_at >= DATE_SUB(NOW(), INTERVAL 7 DAY)
WHERE created_at >= DATE_FORMAT(NOW(), '%Y-%m-01')
SQLite
WHERE created_at >= DATE('now', '-7 days')
WHERE created_at >= DATE('now', 'start of month')
Examples
"Show me users who signed up this month"
SELECT * FROM users
WHERE created_at >= DATE_TRUNC('month', CURRENT_DATE)
ORDER BY created_at DESC;
"Find the top 5 products by sales"
SELECT p.name, SUM(oi.quantity) as total_sold
FROM products p
JOIN order_items oi ON p.id = oi.product_id
GROUP BY p.id, p.name
ORDER BY total_sold DESC
LIMIT 5;
"How many orders per customer?"
SELECT customer_id, COUNT(*) as order_count
FROM orders
GROUP BY customer_id
ORDER BY order_count DESC;
Safety Rules
- Always use LIMIT for exploratory queries (default: 100)
- Never generate DELETE, UPDATE, or DROP unless explicitly requested
- Warn if query might return large result sets
- Use table aliases for readability in JOINs